A composed supervised/unsupervised approach to improve change detection from remote sensing

In this paper a new approach to performing change detection analyses based on a combination of supervised and unsupervised techniques is presented. Two remotely sensed, independently classified images are compared. The change estimation is performed according to the Post Classification Comparison (PCC) method if the posterior probability values are sufficiently high; otherwise a land cover transition matrix, automatically obtained from data, is used. The proposed technique is compared with the traditional PCC approach. It is shown that the new approach correctly detects the “true change” without overestimating the “false” one, while PCC points out “true change” pixels together with a large number of “false changes”.